A real-valued negative selection algorithm with good mathematical foundation is presented to solve some of the drawbacks of previous approach. Specifically, it can produce a good estimate of the optimal number of dete...A real-valued negative selection algorithm with good mathematical foundation is presented to solve some of the drawbacks of previous approach. Specifically, it can produce a good estimate of the optimal number of detectors needed to cover the non-self space, and the maximization of the non-self coverage is done through an optimization algorithm with proven convergence properties. Experiments are performed to validate the assumptions made while designing the algorithm and to evaluate its performance.展开更多
基金Sponsored by the National Natural Science Foundation of China ( Grant No. 60671049 ), the Subject Chief Foundation of Harbin ( Grant No.2003AFXXJ013), the Education Department Research Foundation of Heilongjiang Province(Grant No.10541044,1151G012) and the Postdoctor Founda-tion of Heilongjiang(Grant No.LBH-Z05092).
文摘A real-valued negative selection algorithm with good mathematical foundation is presented to solve some of the drawbacks of previous approach. Specifically, it can produce a good estimate of the optimal number of detectors needed to cover the non-self space, and the maximization of the non-self coverage is done through an optimization algorithm with proven convergence properties. Experiments are performed to validate the assumptions made while designing the algorithm and to evaluate its performance.
